The coherent optical communication systems which employ polarization division multiplexing (PDM) are promising and excellent solutions for high capacity and spectral-efficient communication. The PDM scheme can simply double the transmission rate by utilizing both polarization-orthogonal tributaries at the identical wavelength as multiplexing paths in the fiber. The availability of coherent optical receivers with PDM has been validated by plenty of high capacity experiments [1-5]. Despite of its attractive benefit, there are unavoidable problems in the implementation of the PDM method. The main problematic issue is the crosstalk of the two paths due to the random polarization state variation and polarization mode dispersion (PMD). In order to successfully recover the transmitted data, polarization demultiplexing in optical domain [6-8] or electrical domain [9-11] is needed to separate the mixed signals. In a digital coherent receiver, digital signal processing (DSP) techniques can be employed for polarization demultiplexing. The blind constant modulus algorithm (CMA) and its variants [12-17] are most commonly used, but they are not specially designed for the polarization demultiplexing purpose and might cause the singularity problem [18] of “converge to the same source”, implying that the demultiplexing techniques need to be improved. Supervised adaptive least mean square (LMS) algorithm [19] and single carrier frequency domain equalization (SC-FDE) [20] methods, which require training sequence, can also be implemented. However, they are primarily for channel equalization and behave analogously as CMA in polarization demultiplexing.
Independent component analysis (ICA) was originally proposed for the blind signal separation (BSS) problems, but now it has been developed into broad applications such as voice separation, image processing, bioinformatics, etc. Though widely used in signal processing, its applications in optical communication sphere are rare. Polarization demultiplexing based on ICA method has been explored through maximum likelihood estimation [21-22], in which a gradient optimization algorithm is used with the criterion strictly matching the probability density function (PDF). An approach based on signal higher order statistics makes the ICA demultiplexing algorithm independent of modulation format [23]. There is another ICA method exploiting the magnitude boundedness of digital signal for low symbol rate case [24] and high symbol rate case [25]. The ability of separating the mixed signal components further suggests the potential application of ICA in the newest spatial-division multiplexing technology [26].